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import gradio as gr
from pytube import YouTube
from transformers import pipeline
class GradioInference():
def __init__(self):
self.transcribe_model = pipeline(model='humeur/lab2_id2223')
self.translate_model = pipeline("translation_SV_to_EN", model="Helsinki-NLP/opus-mt-sv-en")
self.yt = None
def __call__(self, link):
if self.yt is None:
self.yt = YouTube(link)
path = self.yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4")
results = self.transcribe_model(path)
results = self.translate_model(results["text"])
return results[0]['translation_text']
def populate_metadata(self, link):
self.yt = YouTube(link)
return self.yt.thumbnail_url, self.yt.title
gio = GradioInference()
title="SWED->EN Youtube Transcriber (Whisper)"
description="Speech to text transcription of Youtube videos using OpenAI's Whisper finetunned for Swedish to English translation"
block = gr.Blocks()
with block:
gr.HTML(
f"""
<div style="text-align: center; max-width: 500px; margin: 0 auto;">
<div>
<h1>{title}</h1>
</div>
<p style="margin-bottom: 10px; font-size: 94%">
{description}
</p>
</div>
"""
)
with gr.Group():
with gr.Box():
link = gr.Textbox(label="YouTube Link")
title = gr.Label(label="Video Title")
with gr.Row().style(equal_height=True):
img = gr.Image(label="Thumbnail")
text = gr.Textbox(label="Transcription", placeholder="Transcription Output", lines=10)
with gr.Row().style(equal_height=True):
btn = gr.Button("Transcribe")
btn.click(gio, inputs=[link], outputs=[text])
link.change(gio.populate_metadata, inputs=[link], outputs=[img, title])
block.launch()
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